U.S. patent application number 15/196117 was filed with the patent office on 2018-01-04 for system, method and recording medium for cognitive proximates.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Laura Irina Rusu, Gandhi Sivakumar.
Application Number | 20180004749 15/196117 |
Document ID | / |
Family ID | 60807547 |
Filed Date | 2018-01-04 |
United States Patent
Application |
20180004749 |
Kind Code |
A1 |
Rusu; Laura Irina ; et
al. |
January 4, 2018 |
SYSTEM, METHOD AND RECORDING MEDIUM FOR COGNITIVE PROXIMATES
Abstract
A cognitive proximate recommendation method, system, and
non-transitory computer readable medium, include identifying a
requested item based on a user request, first extracting a
requested feature and a requested value of the requested feature
for the requested item, and returning a return item from a
plurality of return items stored in the database by: second
extracting a return feature corresponding to the requested feature
for each of the plurality of return items, third extracting a
return value of the return feature, and calculating a proximal
distance between the return value for each of the plurality of
return items and the requested value of the requested item.
Inventors: |
Rusu; Laura Irina;
(Endeavour Hills, AU) ; Sivakumar; Gandhi;
(Bentleigh, AU) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
60807547 |
Appl. No.: |
15/196117 |
Filed: |
June 29, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/2455 20190101;
G06N 5/022 20130101; G06F 16/24578 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06N 99/00 20100101 G06N099/00 |
Claims
1. A cognitive proximate recommendation method including a
database, the method comprising: identifying a requested item based
on a user request; first extracting a requested feature and a
requested value of the requested feature for the requested item;
and returning a return item from a plurality of return items stored
in the database by: second extracting a return feature
corresponding to the requested feature for each of the plurality of
return items; third extracting a return value of the return
feature; and calculating a proximal distance between the return
value for each of the plurality of return items and the requested
value of the requested item.
2. The method of claim 1, wherein the returning returns a ranked
list of the plurality of return items ranked in order of the
proximal distance.
3. The method of claim 1, wherein the returning returns the return
item from the plurality of return items stored in the database
having a closest proximal distance to the requested item.
4. The method of claim 1, wherein the first extracting extracts a
plurality of requested features and a requested value for each of
the plurality of requested features based on the user request, and
wherein the second extracting further extracts a plurality of
return features for each return item corresponding to the requested
feature of the requested item.
5. The method of claim 2, wherein the first extracting extracts a
plurality of requested features and a requested value for each of
the plurality of requested features based on the user request, and
wherein the second extracting further extracts a plurality of
return features for each return item corresponding to the requested
feature of the requested item.
6. The method of claim 4, wherein the first extracting extracts
inter-relationships between each of the plurality of requested
features.
7. The method of claim 4, further comprising sorting the plurality
of return features based on a user input weight for each of the
first extracted plurality of requested features such that the
calculating calculates the proximal distance based on the user
input weight.
8. The method of claim 5, further comprising sorting the plurality
of return features based on a learned weight from a user selection
of the return item from the ranked list for each of the first
extracted plurality of requested features such that the calculating
calculates the proximal distance based on the learned weight.
9. The system of claim 8, wherein the returning returns an updated
ranked list based on the learned weight.
10. The method of claim 1, wherein the identifying identifies the
requested item using a domain-specific taxonomy.
11. The method of claim 1, wherein the user request comprises a
plurality of words, and wherein the first extracting extracts the
requested feature from a semantic relationship between the
plurality of words.
12. The method of claim 1, wherein the return item always has a
closest proximal distance if the requested value matches the return
value.
13. The method of claim 1, wherein the calculating calculates the
proximal distance based on a measurable similarity between the
return value and the requested value.
14. The method of claim 1, wherein the returning recommends a
return item having a return value having a measurable similarity to
the requested value if the third extracting does not extract a
matching return value.
15. A non-transitory computer-readable recording medium recording a
cognitive proximate recommendation program including a database,
the program causing a computer to perform: identifying a requested
item based on a user request; first extracting a requested feature
and a requested value of the requested feature for the requested
item; and returning a return item from a plurality of return items
stored in the database by: second extracting a return feature
corresponding to the requested feature for each of the plurality of
return items; third extracting a return value of the return
feature; and calculating a proximal distance between the return
value for each of the plurality of return items and the requested
value of the requested item.
16. A cognitive proximate recommendation system, said system
comprising: a database; a processor; and a memory, the memory
storing instructions to cause the processor to: identify a
requested item based on a user request; first extract a requested
feature and a requested value of the requested feature for the
requested item; and return a return item from a plurality of return
items stored in the database by: second extracting a return feature
corresponding to the requested feature for each of the plurality of
return items; third extracting a return value of the return
feature; and calculating a proximal distance between the return
value for each of the plurality of return items and the requested
value of the requested item.
17. The system of claim 16, wherein the returning returns a ranked
list of the plurality of return items ranked in order of the
proximal distance.
18. The system of claim 16, wherein the returning returns the
return item from the plurality of return items stored in the
database having a closest proximal distance to the requested
item.
19. The system of claim 16, wherein the first extracting extracts a
plurality of requested features and a requested value for each of
the plurality of requested features based on the user request, and
wherein the second extracting further extracts a plurality of
return features for each return item corresponding to the requested
feature of the requested item.
20. The system of claim 17, wherein the first extracting extracts a
plurality of requested features and a requested value for each of
the plurality of requested features based on the user request, and
wherein the second extracting further extracts a plurality of
return features for each return item corresponding to the requested
feature of the requested item.
Description
BACKGROUND
[0001] The present invention relates generally to a cognitive
proximate recommendation method, and more particularly, but not by
way of limitation, to a system, method, and recording medium for
recommending an item in response to a user query having a lowest
proximal distance between values of extracted features of the items
and the requested item by the user.
[0002] Industry is trending towards so called "cognitive models"
enabled via "Big Data" platforms. Such cognitive models are aimed
to remember prior interactions with users and continuously learn
and refine the responses for future interactions. For example,
cognitive agents are being used for welcoming customers at business
door steps and are expected to evolve intelligent with generations.
Such agents could be enriched for better customer handling by
building the intelligence of the agents.
[0003] Conventional cognitive models for searching and returning
answers have proposed searching for information within social
networks. The conventional search assist techniques receive a
query, such as a partial query, identifies two or more categories
of data that include information satisfying the query, ranks the
identified categories of data based on various selection criteria,
and presents suggested search terms based on the rankings. However,
the conventional techniques relate to a display of the results, not
the selection in that the conventional techniques rank the results
of the query on two or more identified categories and calculate a
quality matrix that is used to display results. The conventional
techniques do not intelligently learn to provide best alternatives
when a null response may occur.
[0004] That is, there is a technical problem in that the
conventional techniques do not consider a cognitive way of
determining a best alternative when a match does not exist and do
not consider using user preferences to weigh values of features of
potential results to intelligently provide a better
alternative.
SUMMARY
[0005] Thus, the inventors have realized a technical solution to
the technical problem to provide significantly more than the
conventional technique of question/answer interaction by
configuring a cognitive analysis of requested items by extracting
the requested features and values of the features by the user and
intelligently providing a closest alternative based on extracting
the same features of alternative items and comparing the values of
the alternatives with user preferences to return the closest
alternative. Thus, the technical solution improves upon the
computer functionality itself by providing better results more
efficiently.
[0006] In an exemplary embodiment, the present invention can
provide a cognitive proximate recommendation method including a
database, the method including identifying a requested item based
on a user request, first extracting a requested feature and a
requested value of the requested feature for the requested item,
and returning a return item from a plurality of return items stored
in the database by: second extracting a return feature
corresponding to the requested feature for each of the plurality of
return items, third extracting a return value of the return
feature, and calculating a proximal distance between the return
value for each of the plurality of return items and the requested
value of the requested item.
[0007] Further, in another exemplary embodiment, the present
invention can provide a non-transitory computer-readable recording
medium recording a cognitive proximate recommendation program
including a database, the program causing a computer to perform:
identifying a requested item based on a user request, first
extracting a requested feature and a requested value of the
requested feature for the requested item, and returning a return
item from a plurality of return items stored in the database by:
second extracting a return feature corresponding to the requested
feature for each of the plurality of return items, third extracting
a return value of the return feature, and calculating a proximal
distance between the return value for each of the plurality of
return items and the requested value of the requested item.
[0008] Even further, in another exemplary embodiment, the present
invention can provide a cognitive proximate recommendation system,
said system including a database, a processor, and a memory, the
memory storing instructions to cause the processor to: identifying
a requested item based on a user request, first extracting a
requested feature and a requested value of the requested feature
for the requested item, and returning a return item from a
plurality of return items stored in the database by: second
extracting a return feature corresponding to the requested feature
for each of the plurality of return items, third extracting a
return value of the return feature, and calculating a proximal
distance between the return value for each of the plurality of
return items and the requested value of the requested item.
[0009] There has thus been outlined, rather broadly, an embodiment
of the invention in order that the detailed description thereof
herein may be better understood, and in order that the present
contribution to the art may be better appreciated. There are, of
course, additional exemplary embodiments of the invention that will
be described below and which will form the subject matter of the
claims appended hereto.
[0010] It is to be understood that the invention is not limited in
its application to the details of construction and to the
arrangements of the components set forth in the following
description or illustrated in the drawings. The invention is
capable of embodiments in addition to those described and of being
practiced and carried out in various ways. Also, it is to be
understood that the phraseology and terminology employed herein, as
well as the abstract, are for the purpose of description and should
not be regarded as limiting.
[0011] As such, those skilled in the art will appreciate that the
conception upon which this disclosure is based may readily be
utilized as a basis for the designing of other structures, methods
and systems for carrying out the several purposes of the present
invention. It is important, therefore, that the claims be regarded
as including such equivalent constructions insofar as they do not
depart from the spirit and scope of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The exemplary aspects of the invention will be better
understood from the following detailed description of the exemplary
embodiments of the invention with reference to the drawings.
[0013] FIG. 1 exemplarily shows a high level flow chart for a
cognitive proximate recommendation method 100.
[0014] FIG. 2 exemplarily shows a high level flow chart for at
least Step 105 of the cognitive proximate recommendation method
100.
[0015] FIG. 3 exemplarily shows one embodiment of method 100.
[0016] FIG. 4 depicts a cloud computing node according to an
embodiment of the present invention.
[0017] FIG. 5 depicts a cloud computing environment according to
another embodiment of the present invention.
[0018] FIG. 6 depicts abstraction model layers according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0019] The invention will now be described with reference to FIGS.
1-6, in which like reference numerals refer to like parts
throughout. It is emphasized that, according to common practice,
the various features of the drawing are not necessarily to scale.
On the contrary, the dimensions of the various features can be
arbitrarily expanded or reduced for clarity. Exemplary embodiments
are provided below for illustration purposes and do not limit the
claims.
[0020] With reference now to FIG. 1, the cognitive proximate
recommendation method 100 includes various steps to provide a user
with a closest item having a lowest (smallest) proximal distance
from the user request. Moreover, the method (system) can benefit
from "learning" from past preferences of the user. As shown in at
least FIG. 4, one or more computers of a computer system 12 can
include a memory 28 having instructions stored in a storage system
to perform the steps of FIG. 1.
[0021] With the use of these various steps and instructions, the
cognitive proximate recommendation method 100 may act in a more
sophisticated and useful fashion, and in a cognitive manner while
giving the impression of mental abilities and processes related to
knowledge, attention, memory, judgment and evaluation, reasoning,
and advanced computation. That is, a system is said to be
"cognitive" if it possesses macro-scale properties--perception,
goal-oriented behavior, learning/memory and action--that
characterize systems (i.e., humans) that all agree are
cognitive.
[0022] Although as shown in FIGS. 4-6 and as described later, the
computer system/server 12 is exemplarily shown in cloud computing
node 10 as a general-purpose computing circuit which may execute in
a layer the cognitive proximate recommendation system method (FIG.
5), it is noted that the present invention can be implemented
outside of the cloud environment.
[0023] Step 101 receives a user request of a question, query,
input, search or the like that the user would like a return. Step
101 can receive the user input by, for example, a Graphical User
Interface (GUI)-based interface enabling the CRUD (Create, Read,
Update, Delete) processes and extension of an industry specific
proximate framework. The user request 101 includes a description of
the item (e.g., item description 140).
[0024] Based on the user request of Step 101, Step 102 identifies
the requested item based on the domain-specific taxonomy database
130 and item description 140.
[0025] The domain specific taxonomy 130 includes information on
lexical relations between words or ontological relations between
concepts such that the item description 140 can be used to identify
the item requested in Step 102 and identify features and values of
the requested item and potential return items in Steps 103 and 104
as described later. The domain-specific taxonomy 130 can be
cognitive in that the domain-specific taxonomy 130 can learn new
lexical relations between words based on the user choosing a
response not ranked the highest.
[0026] For example, if the user request of Step 101 is "I want a
red car with a 2.0-cylinder engine under 20,000 dollars" (e.g., the
item description), Step 102 identifies that the "item" is "a car"
that the user is requesting.
[0027] After the item is identified by Step 102, Step 103 extracts
the features (attributes) of the requested item and the values of
the features input by the user. For example, Step 103 can extract
from the user input of "I want a red car with a 2.0-cylinder engine
under 20,000 dollars" the feature of "color" having a value of
"red", the feature of "cost" having a value of "under 20,000
dollars", and the feature of "engine size" having a value of
"2.0-cylinder".
[0028] That is, each feature comprises one or more values.
Moreover, each feature (or value of a feature) can be dependent or
independent of other features (or values of the same feature,
respectively). For example, a feature of "taste" in food would be
dependent on the feature of "ingredients" and the values thereof.
Alternatively, the engine size ("feature") of a car and the color
("feature") of the car can be independent from each other but the
price ("feature") of the car can be dependent on both the engine
size and the color. Thus, Step 103 can define inter-relationships
of the feature values or define a primary or anchor value for the
linked features (e.g., for "taste" having a value of "sour", a
primary anchor value can be "Tamarind").
[0029] Accordingly, Step 103 has the ability to define a proximal
model of items depending upon the features and cognitive entity
phrasing maps for each attribute.
[0030] Step 104 determines if there is a direct (e.g., exact) match
of an item to the user based on the values of the features
extracted by Step 103. That is, Step 104 would attempt to find a
car ("item") having a color, engine size and cost ("features") of
red, 2.0-cylinder, under 20,000 dollars ("values"),
respectively.
[0031] If "YES", Step 106 returns the direct matched item to the
user.
[0032] If "NO", Step 105 calculates a closest item to the requested
item by the user having a lowest (e.g., smallest) proximal distance
from the requested item (the details by Step 105 are shown in FIG.
2). That is, Step 105a extracts the features of interest identified
in Step 103 from each potential return item from a database 160 and
Step 105b extracts the values of interest corresponding to the
extracted features of interest. In other words, Step 105a extracts
color, engine size, and cost of all potential cars that can be a
return item and Step 105b extracts the values of each of the
extracted features. The database 160 includes potential return
items.
[0033] It is noted that Step 105a does not extract features not
extracted by Step 103. That is, a car, for example, can be
described by a plethora of features but Step 105a extracts the
features of interest (i.e., in this present example, features of
interest would be color, engine size, and cost) corresponding to
the user request to provide the closest item. Thus, features not of
interest (e.g., type of seats, type of transmission, type of tires,
etc.) and values thereof are not extracted by Step 105a/105b.
[0034] Step 105c optionally sorts the features based on a distinct
user preference or input weights to the features 150. That is, the
user ranks (preferences) the features according to importance of
the returned item matching the value of that feature. The distinct
user preference or input weights to the features 150 can include a
pre-configured preference of the user for the features, an
additional query to the user from the GUI for the user to weight
each identified feature by Step 103, a learned preference based on
past user selections of the returned item (e.g., user always picks
a car returned that matches cost instead of color), etc. For
example, the user can assign that the feature of color is three
times as important as cost and twice as important as engine size.
It is noted that Step 105c is optional and absent an affirmative
weight, by default, Step 105 ranks each feature equally.
[0035] Step 105d queries for items fulfilling the value condition
of the features and ranks the returned items based on the lowest
proximal distance between the potential return item and the
requested item.
[0036] Step 105e continuously causes Step 105d to loop to return
potential items to the user as the user response 106. Also, if no
potential return items are found within a threshold proximal
distance, Step 105e causes Step 105d to find potential return items
based on a similarity of values of the potential return items to
the request item. For example, if the extracted feature is color
with a value of "red" for a car (item) and no red cars are in the
database 160, Step 105d can return items to the user having a
somewhat similar color such as "metallic red" (e.g., Step 105d can
use a color scale to calculate similarity between red and a value
of a potential return result). Thus, if the metallic red car is
under 20,000 dollars and has a 2.0-cylinder engine, Step 105d can
return the metallic red car to the user over a car that is, for
example, green based on the preferences of the user. That is, Step
105e dynamically varies (overrides) the proximal distance driven by
priority factors such that, for example, if a preferred flavor was
"sour" and no primary ingredient to give the taste of sour was in
stock (e.g., available), then Step 105e can traverse to the next
closest ingredient and anchor it (e.g., from tamarind to lemon).
Also, since 105e is a feedback loop, the user preferences (e.g.,
weights) are re-examined by Step 105d such that if the closest
alternative was unavailable as in the above example and "lemon" was
suggested, if the user was allergic to lemon, Step 105e would again
cause the next closest alternative to be suggested such as "lime
juice".
[0037] Thereby, Step 106 gives a ranked list of return items to the
user ranked according to the proximal distance from the requested
item.
[0038] In an exemplary use case of the method 100 as shown in FIG.
3, the method 100 can recommend alternative meals for a user based
on user preference at a restaurant that does not have the requested
meal.
[0039] For example, if a user normally orders "Sambar" (e.g.,
identified by Step 102), the method 100 via Step 103 breaks down
the ingredients as features and determines the values of the
ingredients for "Sambar". As shown in FIG. 3, Sambar is broken to
its atomic and composite ingredients and identifies its Anchor
entity as Dhal. It is noted that some items, e.g. Hing Powder and
Water are atomic and could not be broken down further.
[0040] When Step 104 determines that no match exists, Step 105
searches for other available dishes and Step 105a/105b breaks down
their ingredients and performs mapping of values to ingredients. In
this example, Step 105d picks up Dhal Tadkha and Dhal Tomato curry,
as closest dishes based on their Dhal content and the similarity of
other ingredients. As shown in FIG. 3, proximates are flagged as H
(high), M (medium) or L (low) based on their ability to map, e.g. H
when the exact ingredient was found in another dish, L when it was
not found, and M when a similar ingredient (e.g., from the same
family) was found. However, a finer measure of similarity could be
calculated, e.g. as percentage based on ontology tree.
[0041] Based on user preferences factored in Step 105c, Step 105d
eliminates Dhal Tadkha because the user (i.e., Jack) is allergic to
Citric juice, and therefore suggests Dhal Tomato curry as the best
proximate to replace the initial Sambar order. The allergy to
citric juice could be know either directly from the user (e.g. as a
note/input at the time of order), or from the history of the user's
orders, if he was a regular customer, the method 100 could have
learned that some ingredients are systematically avoided by the
user and therefore would exclude them from the suggestions.
[0042] Exemplary Hardware Aspects, Using a Cloud Computing
Environment
[0043] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0044] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0045] Characteristics are as follows:
[0046] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0047] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0048] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0049] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0050] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0051] Service Models are as follows:
[0052] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
circuits through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0053] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0054] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0055] Deployment Models are as follows:
[0056] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0057] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0058] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0059] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0060] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0061] Referring now to FIG. 4, a schematic of an example of a
cloud computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
[0062] In cloud computing node 10 there is a computer system/server
12, which is operational with numerous other general purpose or
special purpose computing system environments or configurations.
Examples of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop circuits, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or circuits, and the like.
[0063] Computer system/server 12 may be described in the general
context of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing circuits that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
circuits.
[0064] As shown in FIG. 4, computer system/server 12 in cloud
computing node 10 is shown in the form of a general-purpose
computing circuit. The components of computer system/server 12 may
include, but are not limited to, one or more processors or
processing units 16, a system memory 28, and a bus 18 that couples
various system components including system memory 28 to processor
16.
[0065] Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0066] Computer system/server 12 typically includes a variety of
computer system readable media. Such media may be any available
media that is accessible by computer system/server 12, and it
includes both volatile and non-volatile media, removable and
non-removable media.
[0067] System memory 28 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
30 and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
memory 28 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
[0068] Program/utility 40, having a set (at least one) of program
modules 42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
[0069] Computer system/server 12 may also communicate with one or
more external circuits 14 such as a keyboard, a pointing circuit, a
display 24, etc.; one or more circuits that enable a user to
interact with computer system/server 12; and/or any circuits (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing circuits. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, circuit drivers, redundant processing units,
external disk drive arrays, RAID systems, tape drives, and data
archival storage systems, etc.
[0070] Referring now to FIG. 5, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 comprises one or more cloud computing nodes 10 with which local
computing circuits used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing circuit.
It is understood that the types of computing circuits 54A-N shown
in FIG. 5 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized circuit over any type of network and/or
network addressable connection (e.g., using a web browser).
[0071] Referring now to FIG. 6, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 5) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 6 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0072] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage circuits
65; and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0073] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0074] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may comprise application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 83 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 84 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 85 provide pre-arrangement
for, and procurement of, cloud computing resources for which a
future requirement is anticipated in accordance with an SLA.
[0075] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and, more
particularly relative to the present invention, the
anti-counterfeiting system 100 and the anti-counterfeiting system
600 described herein.
[0076] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
[0077] Further, Applicant's intent is to encompass the equivalents
of all claim elements, and no amendment to any claim of the present
application should be construed as a disclaimer of any interest in
or right to an equivalent of any element or feature of the amended
claim.
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